Finding small somatic structural variants in exome sequencing data: a machine learning approach

Matthias Kuhn (), Thoralf Stange, Sylvia Herold, Christian Thiede and Ingo Roeder
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Matthias Kuhn: Technische Universität
Thoralf Stange: Technische Universität
Sylvia Herold: Universitätsklinikum der Technischen Universität
Christian Thiede: Universitätsklinikum der Technischen Universität
Ingo Roeder: Technische Universität

Computational Statistics, 2018, vol. 33, issue 3, No 3, 1145-1158

Abstract: Abstract Genetic variation forms the basis for diversity but can as well be harmful and cause diseases, such as tumors. Structural variants (SV) are an example of complex genetic variations that comprise of many nucleotides ranging up to several megabases. Based on recent developments in sequencing technology it has become feasable to elucidate the genetic state of a person’s genes (i.e. the exome) or even the complete genome. Here, a machine learning approach is presented to find small disease-related SVs with the help of sequencing data. The method uses differences in characteristics of mapping patterns between tumor and normal samples at a genomic locus. This way, the method aims to be directly applicable for exome sequencing data to improve detection of SVs since specific SV detection methods are currently lacking. The method has been evaluated based on a simulation study as well as with exome data of patients with acute myeloid leukemia. An implementation of the algorithm is available at https://github.com/lenz99-/svmod .

Keywords: Structural variants; Exome sequencing; Machine learning; Simulation (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-016-0674-2

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